Sonar target simulator

ABSTRACT

A sonar target simulator (“STS”) for training a sonar operator is disclosed. The STS is configured to create a plurality of simulated scenarios within a gaming area having a plurality of environments. The STS includes one or more memory units storing real-world collected data, one or more processing units, and a computer-readable medium. The real-world collected data includes background signatures related to the plurality of simulated environments. The computer-readable medium has encoded thereon computer-executable instructions to cause the one or more processing units to generate a target signature from real-world collected data, generate an environmental model from the real-world data, and combine the target signature with the environmental model to create a simulated scenario, of the plurality of simulated scenarios, for use in the gaming area. In this example, the environmental model corresponds to an environment of the plurality of environments.

BACKGROUND 1. Field

The disclosed systems and methods relate to sonar systems and, moreparticularly, to systems and methods for simulating a sonar device inoperation.

2. Related Art

At present, it is difficult to train acoustic/sonar operators because itis difficult to electronically simulate (i.e. synthesize) the actualoperating conditions of the seas for detecting and tracking vehicles(i.e., targets) within the seas. Some of these difficulties included,among other things, the problems of reproducing the background noise ofthe sea itself as well as the return signals which would be caused byvarious natural phenomena within the sea. For example, sonar targetingsimulators (e.g., submarine targeting simulators) currently rely onmodeled background noise data to hide a target within a simulated sea(i.e., a gaming area). A problem is that acoustic operators of thesesonar targeting simulators are usually able to identify the reproducedand/or modeled (i.e., fake) background noise within the simulatedtraining because it is generally different than the real-worldbackground noise actually produced in the sea. Moreover, existingsubmarine targeting simulators utilize the modeled background noise inconjunction with a number of predetermined simulations. As such,acoustic operators who have previously utilized these submarinetargeting simulators are able to memorize the locations of the simulatedtargets.

Specifically, current submarine target simulators provide Doppler basedinformation by utilizing narrowband techniques to simulate targets ofinterest within a submarine target simulator. Unfortunately, while thisapproach is currently widely utilized, the resulting simulated signalsare simple and typically obvious when compared against real-world data.Moreover, real-world data of target threats are typically decomposed andcharacterized based on narrowband components independently, and thevariations are modeled with simple mathematical models. For example, thecurrent known simulation tools that are utilized to simulate targetssignatures (i.e., the acoustic signature of a given passive sonartarget), background noise and interfering clutter, produce an outputthat has a “look and feel” of the output that is not realistic whencompared to real-world data. As such, the acoustic operators can, ingeneral, easily detect and identify the difference between a simulatedscenario with these known tools and actual real-world in-water data.

Therefore, since the known submarine targeting simulators syntheticallygenerate both the background noise and the target signature and combinethose into a simulated scenario where the acoustic operators are beingtrained, the teaching value of these types of known submarine targetingsimulator is generally diminished or even eliminated since the acousticoperators can, in general, easily detect and identify differencesbetween the synthetic background noise and target signatures simulatedin the scenario because of the differences with the actual real-worldin-water data. As such, there is a need to improve the training value ofthese types of simulators.

SUMMARY

Disclosed is a sonar target simulator (“STS”). The STS is configured tocreate a plurality of simulated scenarios within a gaming area having aplurality of environments. The STS includes one or more memory unitsstoring real-world collected data, one or more processing units, and acomputer-readable medium. The real-world collected data includesbackground signatures related to the plurality of simulatedenvironments. The computer-readable medium has encoded thereoncomputer-executable instructions to cause the one or more processingunits to generate a target signature from real-world collected data,generate an environmental model from the real-world data, and combinethe target signature with the environmental model to create a simulatedscenario, of the plurality of target scenarios, for use in the gamingarea. In this example, the environmental model corresponds to anenvironment of the plurality of environments.

As an example, of operation, the STS performs a method that includesgenerating the target signature from the real-world collected data,generating the environmental model from the real-world collected data,and combining the target signature with the environmental model tocreate the simulated scenario for use in the gaming area.

Other devices, apparatus, systems, methods, features and advantages ofthe invention will be or will become apparent to one with skill in theart upon examination of the following figures and detailed description.It is intended that all such additional systems, methods, features andadvantages be included within this description, be within the scope ofthe invention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE FIGURES

The invention may be better understood by referring to the followingfigures. The components in the figures are not necessarily to scale,emphasis instead being placed upon illustrating the principles of theinvention. In the figures, like reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a system block diagram of an example of an implementation of asonar target simulator (“STS”) in accordance with the presentdisclosure.

FIG. 2 is a system block diagram of an example of an implementation ofthe STS, shown in FIG. 1, within a computing device in accordance withthe present disclosure.

FIG. 3 is a system diagram of an example of an implementation of a sonarsystem including the STS, shown in FIGS. 1 and 2, in accordance with thepresent disclosure.

FIG. 4 is a system block diagram of an example of an implementation ofthe target simulator, shown in FIGS. 1 and 2, in accordance with thepresent disclosure.

FIG. 5 is a flowchart illustrating operation of an example method fortraining a sonar operator with a simulated scenario within a gaming areautilizing STS, shown in FIGS. 1 through 4, in accordance with thepresent disclosure.

DETAILED DESCRIPTION

Disclosed is a sonar target simulator (“STS”) for training a sonaroperator. The STS is configured to create a plurality of simulatedscenarios within a gaming area having a plurality of environments. TheSTS includes one or more memory units storing real-world collected data,one or more processing units, and a computer-readable medium. Thereal-world collected data includes background signatures related to theplurality of simulated environments. The computer-readable medium hasencoded thereon computer-executable instructions to cause the one ormore processing units to generate a target signature from real-worldcollected data, generate an environmental model from the real-worlddata, and combine the target signature with the environmental model tocreate a simulated scenario, of the plurality of target scenarios, foruse in the gaming area. In this example, the environmental modelcorresponds to an environment of the plurality of environments.

As an example, of operation, the STS performs a method that includesgenerating the target signature from the real-world collected data,generating the environmental model from the real-world collected data,and combining the target signature with the environmental model tocreate the simulated scenario for use in the gaming area.

In FIG. 1, a system block diagram of an example of an implementation ofSTS 100 is shown in accordance with the present disclosure. In thisexample, the STS 100 includes one or more memory units 102, a signatureextraction tool 104, and a target simulator 106. The STS 100 may alsoinclude an optional motion model 108 and gaming area 110. In thisexample, the signature extraction tool 104 is in signal communicationwith both the one or more memory units 102 and the target simulator 106via signal paths 112 and 114, respectively. Additionally, the targetsimulator 106 is also in signal communication with the optional motionmodel 108, gaming area 110, and one or more memory units 102 via signalpaths 116, 118, 120, 122, and optional signal path 123, respectively.Moreover, in this example, the signature extraction tool 104 may also beoptionally in signal communication with the one or more memory units 102via signal path 124. Furthermore, the optional motion model 108 may bein signal communication with the one or more memory units 102 via signalpath 125.

In this example, the one or more memory units 102 may include areal-world collected database 126, an optional target signature database128, a database of wave points 130, and an optional simulated scenariodatabase 131. For example, the one or more memory units 102 may be asingle memory unit storing the real-world collected database 126,optional target signature database 128, database of wave points 130, andan optional simulated scenario database 131 or a plurality of memoryunits where a first memory unit may storage the real-world collecteddatabase 126, a second memory unit may store the optional targetsignature database 128, a third memory unit may store the database ofwave points 130, and a fourth memory unit may store the optionalsimulated scenario database 131. In this example, the one or more memoryunits 102 may also be in signal communication with one or more sensors132 via signal path 134.

The real-world collected data in in the real-world collected datadatabase 126 includes real-world collected data of the recorded soundsof the sea that mostly includes background noise of the sea (including,for example, environmental noise and marine life) and occasionallysounds of vehicles traveling through the sea within range of therecording sensors such as, for example, surface ships or submergedvehicles such as submarines. In general, most of the real-worldcollected data will be recorded sound files of background noise that maybe characterized as a background acoustic signature (generally simplyreferred to as a “background signature”) that may include ambient noiselevels produced by the waves, winds, rain, underwater geothermalactivities, magma displacement, marine life, depths of the thermocline,reflections from different underwater landmasses or topologies, etc.Additionally, some of the real-world collected data will includenarrowband and broadband noise produced by surface ship (such as, forexample, passenger, merchant, or military ships) or submarines. Thisnarrowband and broadband noise is “self-produced” noise that is producedand emitted from the surface ship or submarine. In general, broadbandnoise is produced by the propulsion machinery of a surface ship orsubmarine and may include noise produced by propellers, flow noise ofthe hull passing through the water, external fittings, etc.Additionally, narrowband noise is also generally produced by thepropulsion system of the surface ship or submarine and the physicaleffects of cavitation close to the propeller blades. The combination ofthe narrowband and broadband noise produced by a surface ship orsubmarine is defined as the acoustic signature of the surface ship orsubmarine. In this disclosure, the acoustic signatures of any surfaceship or submarine of interest for the sonar operator will be generallyreferred to as a “target signature” since the surface ship or submarineis a “target” to be identified, tracked, and possibly attacked in thesimulated scenario produced by the STS 100.

In general, the real-world collected data is a collection of sound files135 that have been previously recorded by either the one or more sensors132 in signal communication with the STS 100 or one or more sensors fromother surface ships, submarines, or aircraft such as, for example, towedarrays of hydrophones towed underwater in the sea behind other surfaceships or submarines, cylindrical hydrophone arrays, flank arrays, orsonobuoys dropped by aircraft into the sea. In this disclosure, thepreviously recorded sound files of the real-world collected data mayinclude any high fidelity acoustic data files having any digital formatthat may include, for example, WAV, AIFF, AU, PCM, MP3, FLAC, WavPack,TTA, ATRAC, ALAC, MPEG-4, WMA, WMA, WMA lossless, Opus, Vorbis, AAC,proprietary encodings, etc.

As an example, the one or more sensors 132 may be sensors that utilize abeamforming process in which outputs from the hydrophone sensors of ahydrophone array of the one or more sensors 132 are coherently combinedby delaying and summing the outputs to provide enhanced detection andestimation. In underwater applications, sonar operators are trying todetect a target signature of a target that is a directional (i.e.,single direction) signal in the presence of normalized background noisethat is ideally isotropic (i.e., not directional). By arranging thehydrophone array of the one or more sensors 132 in different physicalgeometries and electronically steering them in a particular direction,the sonar operator can increase the signal-to-noise (“SNR”) in a givendirection by rejecting or canceling the noise in other directions. It isappreciated by those of ordinary skill in the art that there are manydifferent kinds of arrays that may be beamformed such as, for example,an equally spaced line, continuous line, circular, cylindrical,spherical, or random sonobouy arrays. In general, the beam patternspecifies the response of these arrays to the variation in direction. Inthe simplest case, an increase in SNR due to the beamformer is calledthe array gain.

It is appreciated by those of ordinary skill in the art that thecircuits, components, modules, and/or devices of, or associated with,the STS 100 are described as being in signal communication with eachother, where signal communication refers to any type of communicationand/or connection between the circuits, components, modules, and/ordevices that allows a circuit, component, module, and/or device to passand/or receive signals and/or information from another circuit,component, module, and/or device. The communication and/or connectionmay be along any signal path between the circuits, components, modules,and/or devices that allows signals and/or information to pass from onecircuit, component, module, and/or device to another and includeswireless or wired signal paths. The signal paths may be physical, suchas, for example, conductive wires, electromagnetic wave guides, cables,attached and/or electromagnetic or mechanically coupled terminals,semi-conductive or dielectric materials or devices, or other similarphysical connections or couplings. Additionally, signal paths may benon-physical such as free-space (in the case of electromagneticpropagation) or information paths through digital components wherecommunication information is passed from one circuit, component, module,and/or device to another in varying digital formats without passingthrough a direct electromagnetic connection.

In an example of operation, the signature extraction tool 104 receivesthe real-world collected data 136 from the real-world collected database126 via signal path 112. The signature extraction tool 104 then extractsa target signature 138 from the real-world collected data 136 and sendsit to the target simulator 106 via signal path 114. In this example, thesignature extraction tool 104 may include threshold modules and/orsoftware that automatically recognizes and selects a target signaturefrom the real-world collected data 136 to generate the target signature138 or the signature extraction tool 104 may include logic and a usercommunication interface that allows a trainer (i.e., a person setting upthe simulated scenario to train other sonar operators) to review thereal-world collected data 136 and manually select (via a targetselection input 140) the target signature 138 from the reviewedreal-world collected data 136. It is appreciated by those of ordinaryskill in the art that the trainer may review the real-world collecteddata 136 utilizing a low frequency analysis and ranging record gram(“LOFARGRAM”) plot of time versus frequency. The trainer may then detectand cut out small portions (i.e., a snippet of the real-world collecteddata) of the audio files of the real-world collected data 136 thatcorrespond to targets of interest to the trainer. As such, in thisexample, the target signature 138 is a snippet of the real-worldcollected data 136 that contains the acoustic signature of a target(i.e., the target signature 138). The acoustic data corresponding to thetarget signature is typically very small compared to the acoustic datacorresponding to background noise of the oceans. As an example, theacoustic data corresponding to the target signature (i.e., the snippetof the target signature 138) may be 20 seconds to 2 minutes long inlength, while the real-world collected data in the real-world collecteddata base 126 may include acoustic background data that may be hundredsof hours or longer.

If the optional target signature database 128 is present, the signatureextraction tool 104 may also send a copy of the target signature 138 tothe optional target signature database 128 for storage for futureretrieval by the signature extraction tool 104 via optional signal path124. In this example, in another simulated scenario, the signatureextraction tool 104 may instead receive a retrieved target signature 142from the optional target signature database 128 and send it as the newtarget signature 138 to the target simulator 106 via signal path 114without having the signature extraction tool 104 modify the receivedtarget signature 142. In this example, the signature extraction tool 104may include logic having hardware and/or software that automaticallyselects a retrieved target signature 142 from the optional targetsignature database 128 to generate the target signature 138 or thesignature extraction tool 104 may include logic and a user communicationinterface that allows the trainer to review the stored target signaturesin the optional target signature database 128 and manually select (againvia the target selection input 140) a retrieved target signature 142from the optional target signature database 128 that will be sent to thetarget simulator 106 (via signal path 114) as the target signature 138.In this example, the optional target signature database 128 may be adatabase of selected snippets of the real-world collected data 136,where the selected snippets correspond to the selected target signatureswithin the real-world collected data 136.

In this example, the optional motion model 108 is a device, component,module, (which may be either hardware, software, or both) capable ofreceiving a plurality of received wave points 144 and, in response,produced a signal of relative velocity values 146 (also referred to aspositional target track values). It is appreciated by those of ordinaryskill in the art that, generally, passive sonar relies on Doppler andbearing information. The optional motion model 108 then performs atarget motion analysis on the bearing tracks to estimate parameters ofbearing and relative velocity of the target signature 138. In otherwords, in this example, the output (i.e., relative velocity values 146)of the motion model 108 is feed to the target simulator 106, and withinthe target simulator 106, observed Doppler and bearing information arederived between all possible pairs of target and receivers tracks (i.e.,sensors 132).

As such, the optional motion model 108 may optionally include a databaseof wave points or be in signal communication with the database of wavepoints 130 (located at the one or more memory units 102), where the wavepoints 144 represent a series of positional points over a course of timefor different assets that include the target that produced the targetsignature 138 and the position of the one or more sensors 132 relativeto the movement of the target. In general, the database of wave points130 may include wave point values from one to three sub-databases ofdata representing the three-dimensional (e.g., the x, y, and zCartesian) coordinates of a target over time, the correspondingthree-dimensional (e.g., the x, y, and z Cartesian) coordinates of theone or more sensors 132, and the resulting relative velocity of thetarget over time in relation to the one or more sensors 132. Typically,these sub-databases would represent three-dimensional matrices where theoptional motion model 108 is configured to calculate a matrix ofrelative velocity values from the matrix of the three-dimensional valuesof the change in position of the moving target versus time and thematrix of the three-dimensional values of the change in position of themoving one or more sensors 132 versus time. In general, it isappreciated by those of ordinary skill in the art that the matrix ofrelative velocity values are Doppler corrected values that arecalculated to compensate for the Doppler shift of the observer (i.e.,the one or more sensors 132) moving relative to the source (i.e., thetarget). In addition, it is also appreciated that a fourth matrix mayalso be included in the Doppler correction where the fourth matrixrepresents the three-dimensional values of the change in position of themedium in which the target and one or more sensors 132 are present. Forexample, the medium in the Ocean seas is moving salt water that may needto be taken into account in the calculation to produce the matrix ofrelative velocity values.

In this example, the reason that the motional model 108 is optional isbecause the optional database of wave points may include eithersub-database of the “raw” positional values of the matrix of thethree-dimensional values of the change in position of the moving targetversus time, the sub-database of the raw positional values of the matrixof the three-dimensional values of the change in position of the movingone or more sensors 132 versus time, and the sub-database of the rawpositional values of the matrix of the three-dimensional values of thechange in position of the moving medium versus time or a database (orsub-database) of the matrix of relative velocity values, or both. As anexample, if the database of wave points 130 includes the matrix ofrelative velocity values, the STS 100 does not need the motion model 108since the database of the matrix of relative velocity values in thedatabase of wave points 130 already has the relative velocity values 146that may simply be retrieved by the target simulator 106 without havingto perform any calculations with the motion model 108.

Alternatively, as an example if the optional motion model 108 ispresent, based on the simulated scenario to be produced by the STS 100,the optional motion model 108 would produce the plurality of relativevelocity values 146 that are sent to the target simulator 106 via signalpath 116. The number of relative velocity values 146 would be based onhow many wave point 144 inputs (from the sub-databases of the matricesof the raw positional values of the moving target, one or more sensors132, and medium) were received by the optional motion model 108 viasignal path 125. As an example, if the optional motion model 108receives five (5) wave points 144, which were created from five (5)unique sound files 135 produced with five (5) of the sensors 132 (orother sensors from other sonar systems on a different ship, submarine,or aircraft that have been previously recorded and stored in thedatabase of wave points 130), the resulting wave points 144 wouldinclude five (5) perceived target signatures of the target (that createdthe target signature 138) going through the gaming area 110. Generally,the optional motion model 108 may include nonlinear filtering methodsthat include, for example, Kalman-Bucy filters and a state space modelfor the motion of the target signature 138. Additionally, in thisexample, the optional motion model 108 may include sub-modules thatallow a user (e.g., a trainer) to define waypoints and a scenario clock.The waypoints is herein defined parameter values that include, forexample, starting positions, velocities, depth/altitude, rate of turn,acceleration, or combination of these parameters and the scenario clockincludes, for example, a duration of the scenario and a sampling rate.Based on the “scenario clock” and waypoints, the output of the motionmodel 108 (i.e., the relative velocity values) will be time, position,velocity, and optionally acceleration all of which are functions oftime.

In general, these receiver (i.e., the one or more sensors 132) andtarget positions and velocities (i.e., the wave points 144) may be inputinto a target motion analysis (“TMA”) module (not shown) within theoptional motion model 108 that estimates the relative velocity(alternatively radial velocity) and relative bearing of the targetsignature 138 to a given sensor 132. In this example, the database ofwave points 130 may be optionally located either in the one or morememory units 102, within the optional motion model 108, or in anotherstorage device (i.e., memory) in signal communication with the optionalmotion model 108.

The optional motion model 108 may also include a user input 147 thatallows the trainer to select the type of position and motion of thetarget signature 138. As an example, if the originally recorded targetsignature 138 was traveling from north-to-south, the trainer may selectthat the target signature 138 be modified to travel from east-to-westfor the simulated scenario 150 via the user input 147.

The target simulator 106 receives the target signature 138, positionalrelative velocity values 146, and a background signature 148 from thesignature extraction tool 104, the optional motion model 108, and one ormore memory units 102 via signal paths 114, 116, and 120, respectively.In response, the target simulator 106 combines the target signature 138,relative velocity values 146, and a background signature 148 andproduces a simulated scenario 150 that is passed to the gaming area 110via signal path 118. The simulated scenario 150 is a created virtualenvironment that is generated by the target simulator 106 to bedisplayed and interacted with within the gaming area 110. Utilizing thesimulated scenario 150, a sonar operator may be trained in detecting,identifying, and tracking different targets (such as, for example, enemyships or submarines) in the simulated scenario 150 within the gamingarea 110. The target simulator 106 may also receive optionalenvironmental data 152 from the one or more memory units 102 (via signalpath 122) so as to modify the environmental conditions of the simulatedscenario 150. In this example, the gaming area 110 is a created digitalvirtual environment or “world” having defined gaming parameters such as,for example, simulation time, extend of virtual area covered by thesimulation, input and output interfaces that allow a sonar operator(shown in FIG. 3) to interact (i.e., “playing a game” within the createdvirtual environment) with the simulated scenario where the simulatedscenario is a simulated scenario since.

In addition to allowing a sonar operator to interact with the STS 100via the gaming area 110, the STS 100 may also send the simulatedscenarios 150 to the optional simulated scenario database 131 viaoptional signal path 123. In this example, the simulated scenariodatabase 131 is a historical simulated scenario database of historicalsimulated scenarios. In this example, the STS 100 may utilize thepreviously recorded simulated scenarios in the future to run standalonescenarios later or maintenance. As an example, a standalone scenario maybe a scenario run by the STS 100 at port (where the ship is not movingand as a result the sensors 132 are not being utilized) or at aland-based area where the STS 100 is part of a land-based simulator thatlacks actual sensors 132. In this example, the simulated scenario 150data would include data that is passed to the gaming area 110, which mayinclude, for example, recorded values for the outputs of whatever numberof sensors 132 are being simulated in a given simulated scenario,optional truth data that would include the actual position over time ofthe simulated target or targets, parameters related to the simulatedscenario, and optional meta-data related to describing the target,description, title, etc.

As an example, the STS 100 allows sonar operators to be trained indetecting different types of target signatures 138 utilizing their ownsonar systems, where the sonar systems collect sound files 135 ofreal-world collected data with their one or more sensors 132, store theminto the real-world collected data database 126 and superimpose targetsignatures 138 on to the background signatures 148 in the real-wouldcollect data 136. The target signatures 138 may be obtained by the sonarsystem or by other external sonar systems that have been previouslyrecorded and stored in the STS 100. As an example, trainers may observethe real-world collected data 136 and make snippets of the real-worldcollected data 136 that contain target signatures 138 that may beoptionally stored in the optional target signature database 128 forlater use in simulated scenarios 150. Alternatively, the optional targetsignature database 128 may include snippet recordings of targetsignatures 138 that were previously recorded by other sonars that areindependent of the sonar of the user (e.g., target signatures recordedby other submarines, ships, or aircraft on different maritime patrols).The target signatures 138 may be a high fidelity as the sonar system (orsystems) are capable of providing. The trainers may then select aspecific target signature 138, an associated position and target motionfor the target signature 138, and an environmental model for thesimulated scenario 150. The target simulator 106 then superimposes themodified target signature 138 to the background signature 148 to createa variety of different simulated scenarios 150 that may be simulated inthe gaming area 110. As such, the STS 100 allows the target signatures138 to be recycled for training the sonar operators because a firsttarget signature that was originally recorded in one place such as, forexample, the Mediterranean sea traveling north-to-south, may be modifiedto become a modified first target signature traveling east-to-west inthe sea of Japan having different environmental spreading and dynamics.

In FIG. 2, a system block diagram of an example of an implementation ofthe STS 100 within a computing device 200 is shown in accordance withthe present disclosure. In this example, the computing device 200includes one or more processors 202, the one or more memories 126, acomputer-readable/storage medium 204, and one or more communicationsinterfaces 206. In this example, the one or more processing units 202,one or more memory units 126, computer-readable medium 204, and one ormore communication interfaces 206 are in signal communication andoperatively connected with each other via a bus signal path 208, whichmay include one or more of a system bus, a data bus, an address bus, aPeripheral Component Interconnect (“PCI”) bus, a Mini-PCI bus, and anyvariety of local, peripheral, and/or independent buses. Thecomputer-readable medium 204 includes encoded thereoncomputer-executable instructions that cause the one or more processors202 to generate a target signature 138 from the real-world collecteddata 136, generate an environmental model from the real-world collecteddata 136 and optional environmental data 152, and combine the targetsignature 138 with the environmental model to create a simulatedscenario 150 for use in the gaming area 110. In this example, the STS100 may include the one or more memory units 126 from the one or moreprocessing units 202 and the signature extraction tool 104, targetsimulator 106, optional motion model 108, and gaming area 110 of thecomputer-readable medium 204.

As utilized herein, the one or more processing units 202 may represent,for example, a central processing unit (“CPU”)-type processing unit, agraphics processing unit (“GPU”)-type processing unit, afield-programmable gate array (“FPGA”), another class of digital signalprocessor (“DSP”), or other hardware logic components that may, in someinstances, be driven by a CPU. For example, and without limitation,illustrative types of hardware logic components that may be utilizedinclude Application-Specific Integrated Circuits (“ASICs”),Application-Specific Standard Products (“ASSPs”), System-on-a-ChipSystems (“SOCs”), Complex Programmable Logic Devices (“CPLDs”), etc.

As utilized herein, computer-readable medium 204 may store instructionsexecutable by the one or more processing units 202. Thecomputer-readable medium 204 may also store instructions executable byexternal processing units (not shown) such as by an external CPU, anexternal GPU, and/or executable by an external accelerator, such as anFPGA type accelerator, a DSP type accelerator, or any other internal orexternal accelerator. In various examples, at least one CPU, GPU, and/oraccelerator is incorporated in the computing device 200, while in someexamples one or more of a CPU, GPU, and/or accelerator is external to acomputing device 200.

The computer-readable medium 204 may include computer storage mediaand/or communication media. Computer storage media may include one ormore of volatile memory, nonvolatile memory, and/or other persistentand/or auxiliary computer storage media, removable and non-removablecomputer storage media implemented in any method or technology forstorage of information such as computer-readable instructions, datastructures, program modules, or other data. Thus, computer storage mediaincludes tangible and/or physical forms of media included in a deviceand/or hardware component that is part of a device or external to adevice, including but not limited to random-access memory (“RAM”),static random-access memory (“SRAM”), dynamic random-access memory(“DRAM”), phase change memory (“PCM”), read-only memory (“ROM”),erasable programmable read-only memory (“EPROM”), electrically erasableprogrammable read-only memory (“EEPROM”), flash memory, compact discread-only memory (“CD-ROM”), digital versatile disks (“DVDs”), opticalcards or other optical storage media, magnetic cassettes, magnetic tape,magnetic disk storage, magnetic cards or other magnetic storage devicesor media, solid-state memory devices, storage arrays, network attachedstorage, storage area networks, hosted computer storage or any otherstorage memory, storage device, and/or storage medium that can be usedto store and maintain information for access by a computing device.

In contrast to the computer storage medium 204, communication media mayembody computer-readable instructions, data structures, program modules,or other data in a modulated data signal, such as a carrier wave, orother transmission mechanism. As defined herein, computer storage medium204 does not include communication media. That is, computer storagemedia does not include communications media consisting solely of amodulated data signal, a carrier wave, or a propagated signal, per se.

The one or more communication interfaces 206 may represent, for example,network interface controllers (“NICs”) or other types of transceiverdevices to send and receive communications over a network.

In this example, the computer-readable medium 204 includes a data store210. In some examples, the data store 210 may include data storage suchas a database, data warehouse, or other type of structured orunstructured data storage for operation of computing device 200 and STS100. In some examples, the data store 210 may include a corpus and/or arelational database with one or more tables, indices, stored procedures,and so forth to enable data access of the STS 100 by a user including,for example, one or more of hypertext markup language (“HTML”) tables,resource description framework (“RDF”) tables, web ontology language(“OWL”) tables, and/or extensible markup language (“XML”) tables.

The data store 210 may store data for the operations of processes,applications, components, and/or modules stored in computer-readablemedium 204 (such as the STS 100) and/or executed by the one or moreprocessing units 202 and/or accelerator(s). As an example, the datastore 210 may store session data 212, profile data 214, and/or otherdata 215. The session data 212 may include data relating to the trainingsessions performed by the STS 100, and activity that occurred in thetraining session and/or other data related to when and how the trainingsession where conducted or hosted by the STS 100. Examples of profiledata 214 include, but are not limited to, an identity (“ID”) and otherdata of the sonar operator being trained.

Alternately, some or all of the above-referenced data may be stored onthe separate one or more memory units 126 on board the one or moreprocessing units 202 such as, for example, a memory on board a CPU-typeprocessor, a GPU-type processor, an FPGA-type accelerator, a DSP-typeaccelerator, and/or another accelerator. In this example, thecomputer-readable medium 204 also includes an operating system 216 andapplication programming interfaces (“APIs”) 218 configured to expose thefunctionality and the data of the STS 100 to external devices associatedwith the computing device 200 via the one or more communicationinterfaces 206. Additionally, the computer-readable medium 204 mayinclude one or more modules such as the server module 220, input module222, and output module 224, although the number of illustrated modulesis just an example, and the number may vary higher or lower. That is,the functionality described in this disclosure in association with theillustrated modules in the computing device 200 may be performed by afewer number of modules or a larger number of modules on one device orspread across multiple devices. In this example, the output module 224may be in signal communication with one or more output devices (such as,for example, one or more displays and sound speakers) that allow thesonar operator to see and hear data related to the simulated scenario150 within the gaming area 110. Similarly, the input module 222 may bein signal communication with one or more input devices (such as, forexample, a keyboard, mouse, general pointing device, or touch screen)that allow the sonar operator to respond and input commands to thecomputing device 200.

Turning to FIG. 3, a system diagram of an example of an implementationof a sonar system 300 including the STS 100 is shown in accordance withthe present disclosure. The sonar system 300 may also include the one ormore sensors 132, the computing device 200, one or more input devices302 (in signal communication with the input module 222), and one or moreoutput devices 304 (in signal commination with the output module 224).In this example, a first display 306, second display 308, and thirddisplay 310 are shown to provide information to a sonar operator 312that is being trained on the sonar system 300. In general, for passivesonar systems, the three main parameters of interest are time,frequency, and bearing. Since three-dimensional data is usuallydifficult to visualize and analyze, it is usually displayed in threetypes of formats—bearing time, bearing frequency, and time frequency.The bearing time is obtained by integrating over frequency, which isuseful for target signatures with significant broadband characteristics.The bearing time is displayed as a bearing time recorder (“BTR”) plot oftime versus bearing (i.e., the horizontal angle). The bearing frequencyis obtained at particular intervals of time, which is effective fortarget signatures with strong stable spectral lines. The time frequencyis displayed as a frequency azimuth (“FRAZ”) plot of frequency versusbearing. The time frequency is obtained by recording the narrow beamspectrum of the target signature in a given beam as a function of time.In this example, the first display 306 may be a BTR display, the seconddisplay 308 may be FRAZ display, and the third display 310 may be aLOFARGRAM (also known as “LOFAR” gram) display. It is appreciated thatthe one or more output devices 304 may also include other types ofdisplays but in general for passive sonars (or training on passive andnon-active use of a sonar that is both passive and active), sonaroperators 312 will only need the output information from BTR, FRAZ, andLOFARGRAM displays to make an informed decision as to whether a targetsignature has been detected and, then start the process of classifyingthe detected target signature. In general, the combination of the one ormore output devices 304 and one or more input devices 302 allow thesonar operator 312 to interact with the simulated scenario within thegaming area 110 of the STS 100.

In FIG. 4, a system block diagram of an example of an implementation ofthe target simulator 106 is shown in accordance with the presentdisclosure. The target simulator 106 may include an environmental model400, target signature phase manipulation module 402, environmentalmanipulation module 406, and sensor simulator module 408. In thisexample, the environmental manipulation module 406 may be in signalcommunication with the environmental model 400, target signature phasemanipulation module 402, and sensor simulator 408, via signal paths 410,412, and 414, respectively. The target signature phase manipulationmodule 402 may be in signal communication with the signature extractiontool 104 and optional motion model 108 via signal paths 114 and 116,respectively. The environmental model 400 may also optionally be insignal communication with the one or more memory units 102 via signalpath 122.

In this example, the environmental model 400 receives the backgroundsignature 148 from the real-world collected data database 126 (at theone or more memory units 102) and generates an environment for thesimulated scenario 150 based on the background signature 148. In thisexample, background signature 148 from the one or more memory units 102may be feed directly to the environmental model 400 that passes itwithout modification to the environment manipulation module 406.Alternatively, the environmental model 400 may modify the backgroundsignature 148 to create an environmental base scenario 418 that is feedinto the environment manipulation module 406, where the environmentalbase scenario 418 may be generated, for example, from parameters likeWenz Curves.

The resulting environmental base scenario 418 is sent to the environmentmanipulation module 406 via signal path 410. For example, if thebackground signature 148 includes recorded background noise that wasrecorded in different parts of the North Atlantic Ocean, the resultingenvironmental base scenario 418 would include the background signature148 recorded in one part of the North Atlantic Ocean. If the one or morememory units 102 includes additional stored information and datarelating to different geographic area (such as, for example, underwatereffects on sound propagation for specific seas around the world) thatinformation (i.e., environmental data 152) may also be sent to theenvironmental model 400 for use in generating the environmental basescenario 418. Additionally, the environmental model 400 may beconfigured to receive an optional environmental selection input 420 froma trainer programming the simulated scenario 150. As an example, if theone or more memory units 102 includes additional stored information anddata relating to different geographic area, the trainer may review thebackground signature 148 and instruct the environmental model 400 toutilize the background signature 148 from another part of the NorthAtlantic Ocean in generating the environmental base scenario 418.Moreover, the environmental model 400 may allow the background signature148 to be modified so as to generate an environmental base scenario 418from another part of the world. For example, the environmental model 400may modify the background signature 148 that was recorded in the NorthAtlantic Ocean to appear as if it were recorded, instead, for example,in the Indian Ocean, South Pacific Ocean, South Atlantic, Gulf ofMexico, Mediterranean Sea, or Sea of Japan. In this example, thegenerated environmental base scenario 418 may be appear to have beenrecorded in a different geographic location than the original locationwhere the background signature 148 was recorded. Furthermore, theenvironmental model 400 may modify the location of the backgroundsignature 148 in response to a selection of the trainer via theenvironmental selection input 420.

In this example, the relative velocity values 146 produced by theoptional motion model 108 are feed into the target signature phasemanipulation module 402, where the relative velocity values 146 includecompression or expansion factors being on a radial velocity of thetarget signature 138. In general, it appreciated by those of ordinaryskill in the art that for a narrowband signal, the Doppler effect iscompressed based on radial velocity (alternatively the relativevelocity), where for positive radial velocity, the observed frequenciesseem higher than they actually are and for negative radial velocity, theobserved frequencies seem lower than they are. Moreover, for broadbandsignals, the same principles for narrowband signals apply. Furthermore,the perceived bandwidth will also change, where for positive radialvelocity, the bandwidth will shrink, and for negative radial velocities,the bandwidth will increase.

The target signature phase manipulation module 402 receives the targetsignature 138 (via signal path 114) from the signature extraction tool104 and the relative velocity values 146 and modifies the targetsignature 138 based on the relative velocity values 146. The targetsignature phase manipulation module 402 then produces a modified targetsignature 424 that is passed to the environmental manipulation module406 via signal path 412. In this example, the target signature phasemanipulation module 402 may include components, circuitry, and ormodules that are configured to filter and decimate, Doppler expand orcompress, inverse beamform, and scale the target signature 138 toproduce the modified target signature 424.

The environmental manipulation module 406 receives the environmentalbase scenario 418 and the modified target signature 424 and superimposesthe modified target signature 424 on to the environmental base scenario418. The environmental manipulation module 406 then generates rawsimulated scenario data 426 that is passed to the sensor simulator 408via signal path 414. In this example, based on the simulated position ofthe sensors 132 in the gaming area 110, the environmental manipulationmodule 406 will alter the perceived SNR of the modified target signature424 based on the range and bearing of the target. The resulting targetsignature observed is added to the background signature 148 orenvironmental based scenario 418 (i.e., the modified backgroundsignature 148 by the environmental model 400) to produce the rawsimulated scenario data 426.

The sensor simulator 408 may include a beamforming module (not shown)and narrowband processing module (not shown) that receive the rawsimulated scenario data 426 and modify it based feedback from the sonaroperator 312 being trained via an operator input 428. Examples of theoperator input 428 may include, for example, adjustments made by thesonar operator 312 to one or more sensors 132 and/or acoustic processingas the sonar operator 312 attempts to detect the target signature 138.The sensor simulator 408 then produces the simulated scenario 150 thatis passed to the gaming area 110 where the sonar operator 312 interactswith the simulated scenario 150 in a training session.

Prior to the sensor simulator 408, the background signature and targetsignatures may be processed by a microprocessor (not shown) thatreceives the raw simulated scenario data 426 and formats it forconversion by the sensor simulator 408. In general, the sensor simulator408 converts the numerically represented physical data of the rawsimulated scenario data 426 into a data having a sensor format thatincludes, for example, volts, counts, beamformed information thatsimulates whatever the actual format of the one or more of the sensors132 that are being simulated is.

FIG. 5 is a flowchart 500 is shown illustrating operation of an examplemethod for training the sonar operator 312 with a simulated scenario 150within the gaming area 110 utilizing STS 100 in accordance with thepresent disclosure. In general, the method starts 502 by generating atarget signature from real-world collected data that includes:extracting, in step 504, an initial target signature 138 from thereal-world collected data 136 or a target signature data 138 from theoptional target signature database 128; determining a target motionmodel, in step 506, for the initial target signature 138; determining,in step 508, a radial velocity for the initial target signature 138based on the target motion model; and modifying, in step 510, theinitial target signature based on the motion model to create a modifiedtarget signature 424. The process then simultaneously (or approximatelysimultaneously) generates, in step 512, an environmental model (i.e.,the environmental based scenario 418) from the real-world collected data136. The method then combines, in step 514, the environmental model 400with the modified target signature 424 to create the simulated scenario150. The method then ends 516.

It will be understood that various aspects or details of the inventionmay be changed without departing from the scope of the invention. It isnot exhaustive and does not limit the claimed inventions to the preciseform disclosed. Furthermore, the foregoing description is for thepurpose of illustration only, and not for the purpose of limitation.Modifications and variations are possible in light of the abovedescription or may be acquired from practicing the invention. The claimsand their equivalents define the scope of the invention.

The flowchart and block diagrams in the different depicted example ofimplementations illustrate the architecture, functionality, andoperation of some possible implementations of apparatuses and methods inan illustrative example. In this regard, each block in the flowchart orblock diagrams may represent a module, a segment, a function, a portionof an operation or step, some combination thereof.

In some alternative implementations of an illustrative example, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be executed substantially concurrently, or the blocks maysometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added in addition tothe illustrated blocks in a flowchart or block diagram.

The description of the different illustrative examples has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the examples in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different illustrative examplesmay provide different features as compared to other desirable examples.The example, or examples, selected are chosen and described in order tobest explain the principles of the examples, the practical application,and to enable others of ordinary skill in the art to understand thedisclosure for various examples with various modifications as are suitedto the particular use contemplated.

What is claimed is:
 1. A sonar target simulator (“STS”) comprising: oneor more memory units configured to store real-world collected data,wherein the real-world collected data was previously recorded; and oneor more processing units, wherein the one or more processing units areconfigured to create a gaming area configured to be displayed on adisplay for use by a sonar operator and operable with one or more usercontrols, and generate a target signature from the real-world collecteddata, generate a background signature from the real-world collecteddata, modify the target signature with relative velocity values of thetarget signature, and combine the background signature and the modifiedtarget signature to create a simulated scenario for use in the gamingarea.
 2. The STS of claim 1 wherein the sonar operator selects aselected location in an attempt to select location information of thetarget signature and wherein the one or more processing units display tothe sonar operator data regarding the accuracy of the selected location.3. A method for creating a simulated scenario within a gaming areautilizing a sonar target simulator (“STS”), the method comprising:generating a target signature from real-world collected data; generatingan environmental model from the real-world collected data; and combiningthe target signature with the environmental model to create thesimulated scenario for use in the gaming area.
 4. The method of claim 3,wherein generating the target signature includes extracting an initialtarget signature from the real-world collected data or a targetsignature data.
 5. The method of claim 4, wherein extracting the initialtarget signature includes receiving a target selection input from atrainer.
 6. The method of claim 4, wherein generating the targetsignature further includes modifying the initial target signature with amotion model to create a modified target signature.
 7. The method ofclaim 6, wherein modifying the initial target signature includesmanipulating the initial target signature with relative velocity valuesof the initial target signature.
 8. The method of claim 7, wherein therelative velocity values are generated from a matrix of positionalvalues of the target signature and a matrix of positional values of oneor more sensors.
 9. The method of claim 8, wherein generating theenvironmental model includes receiving a background signature from thecollected real-world data.
 10. The method of claim 9, wherein generatingthe environmental model further includes receiving an environmentselection input from a trainer.
 11. The method of claim 9, whereincombining the target signature with the environmental model includesmanipulating the environmental model with the modified target signature.12. A sonar target simulator (“STS”), wherein the STS is configured tocreate a plurality of simulated scenarios within a gaming area having aplurality of environments, the STS comprising: one or more memory unitsstoring real-world collected data, wherein the real-world collected dataincludes background signatures related to the plurality of environments;one or more processing units; and a computer-readable medium havingencoded thereon computer-executable instructions to cause the one ormore processing units to generate a target signature from the real-worldcollected data, generate an environmental model from the real-worldcollected data, wherein the environmental model corresponds to anenvironment of the plurality of environments, and combine the targetsignature with the environmental model to create a simulated scenario,of the plurality of simulated scenarios, for use in the gaming area. 13.The STS of claim 12, wherein generating the target signature includesextracting an initial target signature from the real-world collecteddata or a target signature data.
 14. The STS of claim 13, whereinextracting the initial target signature includes receiving a targetselection input from a trainer.
 15. The STS of claim 13, whereingenerating the target signature further includes modifying the initialtarget signature with relative velocity values of the initial targetsignature to create a modified target signature.
 16. The STS of claim15, wherein the relative velocity values are generated from a matrix ofpositional values of the target signature and a matrix of positionalvalues of one or more sensors.
 17. The STS of claim 16, wherein thecomputer-executable instructions further cause the one or moreprocessing units to generate a motion model, wherein modifying theinitial target signature includes modifying the initial target signaturebased on the motion model.
 18. The STS of claim 17, wherein generatingthe environmental model includes receiving a background signature fromthe collected real-world data.
 19. The STS of claim 18, whereingenerating the environmental model further includes receiving anenvironment selection input from a trainer.
 20. The STS of claim 18,wherein combining the target signature with the environmental modelincludes manipulating the environmental model with the modified initialtarget signature.